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S. Lindbergh and J. Radke
management (Bodin and Crona 2009; Chaffin et al. 2016; Cumming et al. 2006;
Mills et al. 2014; Pahl-Wostl et al. 2010; Stein et al. 2011), for optimizing emergency response collaborative networks (Bodin and Nohrstedt 2016; Nowell et al.
2018), and for improving climate adaptation governance structures (Calliari et al.
2019; Ceddia et al. 2017; Vignola et al. 2013), but have rarely been applied to CI
governance systems although the organizational interdependencies.
Nuss et al. (2016) and Mueller et al. (2017) present an overview of supply chain
mapping through network analysis and discuss how this modeling process can be
combined with criticality and scenario analysis to develop a comprehensive overview
of risk for a specific good or service. They argue that although supply chains are often
depicted as conversive and dispersive graphs, leading to the distribution of a product,
they closely resemble networks. Even though network science has been developing
models to help understand the world wide web, study trophic chains, and public
utilities, it is a fairly new application to supply chain analysis. Because the data
and information on the goods and services are usually non-disclosed and companyspecific, network topology of supply chain rarely attains published research. Nevertheless, topological models of supply chain are interesting approaches to untangle
organizational interdependency complexities.
A combination of topological metrics and hazard information such as severity of
exposure of assets operated, owned or regulated by specific stakeholders, can measure
indirect organizational exposure of CI and develop a better sense of CI vulnerability
paths as socio-technical and socio-ecological systems. The connectedness of physical
and organizational dimensions of CI can help identify new types of exposure that are
usually non-disclosed and addressed as corporate risk by individual companies (Vitali
et al. 2011). By developing CI topological models of organizational interdependency
and applying community detection techniques (Fortunato 2010; Newman and Girvan
2004), groups of organizations that are currently exposed to similar hazards and
similar planning horizons can be delineated for targeted emergency management
and adaptive governance. These new communities represent new scales of collaborative action for resilience which can be retrieved from short- and long-term hazard
exposure information (geospatial proximity) together with centrality and community
detection metrics (topological proximity). Finally mapping organizational interconnectivity of CI is an essential step to identify collaborative networks with potential
to improve cross-boundary risk governance.
Although improvements in stakeholder engagement are presented as the baseline
for CI risk governance and policy (DHS 2013; Djalante 2012; Goldthau and Sovacool
2012), collaborative learning through the process is considered as a mere side-effect.
It is difficult to measure collaborative learning and understand when and how it can
be effective. Studies based on governance and collective action in social-ecological
systems propose network representations of stakeholders to formulate meaningful
collaborative groups to tackle complex environmental problems (Hamilton, Fischer,
and Ager 2019; Bodin and Nohrstedt 2016; Ceddia et al. 2017). Some authors point
to the limitations of stakeholder partnership platforms when conflicting interests
and power relationships are ignored. The result is often symbolic outcomes with
theoretical guidelines and “wish lists” that have been criticized in the context of
S. Lindbergh and J. Radke
management (Bodin and Crona 2009; Chaffin et al. 2016; Cumming et al. 2006;
Mills et al. 2014; Pahl-Wostl et al. 2010; Stein et al. 2011), for optimizing emergency response collaborative networks (Bodin and Nohrstedt 2016; Nowell et al.
2018), and for improving climate adaptation governance structures (Calliari et al.
2019; Ceddia et al. 2017; Vignola et al. 2013), but have rarely been applied to CI
governance systems although the organizational interdependencies.
Nuss et al. (2016) and Mueller et al. (2017) present an overview of supply chain
mapping through network analysis and discuss how this modeling process can be
combined with criticality and scenario analysis to develop a comprehensive overview
of risk for a specific good or service. They argue that although supply chains are often
depicted as conversive and dispersive graphs, leading to the distribution of a product,
they closely resemble networks. Even though network science has been developing
models to help understand the world wide web, study trophic chains, and public
utilities, it is a fairly new application to supply chain analysis. Because the data
and information on the goods and services are usually non-disclosed and companyspecific, network topology of supply chain rarely attains published research. Nevertheless, topological models of supply chain are interesting approaches to untangle
organizational interdependency complexities.
A combination of topological metrics and hazard information such as severity of
exposure of assets operated, owned or regulated by specific stakeholders, can measure
indirect organizational exposure of CI and develop a better sense of CI vulnerability
paths as socio-technical and socio-ecological systems. The connectedness of physical
and organizational dimensions of CI can help identify new types of exposure that are
usually non-disclosed and addressed as corporate risk by individual companies (Vitali
et al. 2011). By developing CI topological models of organizational interdependency
and applying community detection techniques (Fortunato 2010; Newman and Girvan
2004), groups of organizations that are currently exposed to similar hazards and
similar planning horizons can be delineated for targeted emergency management
and adaptive governance. These new communities represent new scales of collaborative action for resilience which can be retrieved from short- and long-term hazard
exposure information (geospatial proximity) together with centrality and community
detection metrics (topological proximity). Finally mapping organizational interconnectivity of CI is an essential step to identify collaborative networks with potential
to improve cross-boundary risk governance.
Although improvements in stakeholder engagement are presented as the baseline
for CI risk governance and policy (DHS 2013; Djalante 2012; Goldthau and Sovacool
2012), collaborative learning through the process is considered as a mere side-effect.
It is difficult to measure collaborative learning and understand when and how it can
be effective. Studies based on governance and collective action in social-ecological
systems propose network representations of stakeholders to formulate meaningful
collaborative groups to tackle complex environmental problems (Hamilton, Fischer,
and Ager 2019; Bodin and Nohrstedt 2016; Ceddia et al. 2017). Some authors point
to the limitations of stakeholder partnership platforms when conflicting interests
and power relationships are ignored. The result is often symbolic outcomes with
theoretical guidelines and “wish lists” that have been criticized in the context of